PSNR

Compute peak signal-to-noise ratio (PSNR) between images

Library

Statistics

visionstatistics

Description

The PSNR block computes the peak signal-to-noise ratio, in decibels,
between two images. This ratio is often used as a quality measurement
between the original and a compressed image. The higher the PSNR,
the better the quality of the compressed, or reconstructed image.

The Mean Square Error (MSE) and the Peak
Signal to Noise Ratio (PSNR) are the two error metrics
used to compare image compression quality. The MSE represents the
cumulative squared error between the compressed and the original image,
whereas PSNR represents a measure of the peak error. The lower the
value of MSE, the lower the error.

To compute the PSNR, the block first calculates the mean-squared
error using the following equation:

MSE=∑M,N[I1(m,n)−I2(m,n)]2M*N

In the previous equation, M and N are
the number of rows and columns in the input images, respectively.
Then the block computes the PSNR using the following equation:

PSNR=10log10(R2MSE)

In the previous equation, R is the maximum
fluctuation in the input image data type. For example, if the input
image has a double-precision floating-point data type, then R is
1. If it has an 8-bit unsigned integer data type, R is
255, etc.

Recommendation for Computing PSNR for Color Images

Different approaches exist for computing the PSNR of a color
image. Because the human eye is most sensitive to luma information,
compute the PSNR for color images by converting the image to a color
space that separates the intensity (luma) channel, such as YCbCr.
The Y (luma), in YCbCr represents a weighted average of R, G, and
B. G is given the most weight, again because the human eye perceives
it most easily. With this consideration, compute the PSNR only on
the luma channel.

Ports

Port

Output

Supported Data Types

Complex
Values Supported

I1

Scalar, vector, or matrix of intensity values

Double-precision floating point

Single-precision floating point

Fixed point

8-, 16-, and 32-bit signed integer

8-, 16-, and 32-bit unsigned integer

No

I2

Scalar, vector, or matrix of intensity values

Same as I1 port

No

Output

Scalar value that represents the PSNR

Double-precision floating point

For fixed-point or integer input, the block output
is double-precision floating point. Otherwise, the block input and
output are the same data type.